EasyFlinkCEP

Giatrakos, Nikos; Kougioumtzi, Eleni; Kontaxakis, Antonios; Deligiannakis, Antonios; Kotidis, Yannis

doi:10.1145/3459637.3482094

Cited by 2 publications

(2 citation statements)

References 23 publications

(26 reference statements)

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“…Acquisition Function Choice: For training the Throughput Regressor of the RATS Modeler, an LCB acquisition function should be used. LCB favors exploration vs exploitation (Section II-E) and better captures trends in throughput instead of absolute values [14]. This seems to favor accurate estimations of a ratio, such as T HP Ratio i (k).…”

Section: Insights and Lessons Learnedmentioning

confidence: 99%

“…A different, relevant line of work comes from workload optimization over cluster, database and Big Data infrastructures [20]. EasyFlinkCEP [14] employs Bayesian Optimization to auto-tune the optimal parallelism of FlinkCEP programs. Wayeb [21] applies BO to find (near-)optimal training configurations for the Complex Event Forecasting Engine.…”

Section: Related Workmentioning

confidence: 99%

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Optimizing Resource Allocation for Tumor Simulations over HPC Infrastructures

Streviniotis,

Giatrakos,

Kotidis

et al. 2023

2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)

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We introduce RATS (Resource Allocator for Tumor Simulations), the first o ptimizer f or t he e xecution o f t umor simulations over HPC infrastructures. The optimization framework of RATS incorporates 3 vital performance criteria (i) expected utility of a simulation in terms of effective drug combination on the simulated tumor, (ii) simulation execution time and (iii) number of cores required for achieving that execution time. RATS is to be used by life scientists at the Barcelona Supercomputing Center to not only remove the burden of blindly guessing the core hours we need to reserve from HPC admins to study various tumor treatment methodologies, but also to help in more rapidly distinguishing effective drug combinations, thus, potentially cutting time to market for new cancer therapies.

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Section: Insights and Lessons Learnedmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Optimizing Resource Allocation for Tumor Simulations over HPC Infrastructures

Streviniotis,

Giatrakos,

Kotidis

et al. 2023

2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)

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Optimizing complex event forecasting

Stavropoulos

Alevizos

Giatrakos

et al. 2022

Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems

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In Complex Event Recognition (CER), applications express business rules in the form of patterns and deploy them in a CER Engine which seeks the occurrence of such patterns on incoming streams. This is useful for practical applications which rely on the timely detection of patterns to support critical decisions. One step further, stakeholders want to act proactively, accurately forecasting the occurrence of patterns on raw streams well ahead of time to better schedule their decisions. This calls for making the transition from CER to Complex Event Forecasting (CEF). In CEF, stochastic models of future behavior are embedded into the event processing loop to project into the future the sequence of events that have occurred so far and to estimate the likelihood of the imminent occurrence of more complex patterns. CEF performance engages the stochastic model's training speed and forecast accuracy. In turn, these performance dimensions are affected by few parameters. However, CEF parameter tuning so that optimal CEF performance is achieved is a non-trivial task. This is due to the fact that there is an infinite number of possible parameter combinations, each affecting CEF performance in ways which are hard to predict. In this work, we introduce the first CEF Optimizer that gracefully automates CEF parameter tuning decisions, rapidly cherry picking good CEF configurations. We detail the novel internal architecture of our CEF Optimizer and present an elaborate empirical analysis on two applications that illustrates the effectiveness of our optimization approach. CCS CONCEPTS• Information systems → Data management systems; • Theory of computation → Formal languages and automata theory; • Mathematics of computing → Bayesian computation.

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EasyFlinkCEP

Cited by 2 publications

References 23 publications

Optimizing Resource Allocation for Tumor Simulations over HPC Infrastructures

Optimizing Resource Allocation for Tumor Simulations over HPC Infrastructures

Optimizing complex event forecasting

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